The Main Differences That Distinguish Data Science, Data Analytics And Machine Learning

Jargon can be intimidating and seemingly impenetrable to the uninformed. While technological language is an unfortunate side effect of the complicated world of machines, those involved with computers, data, and everything else related to technology are not doing themselves any favors by using all that redundant-sounding terminology. Let's review the field of Data Science and Data Analytics.

 

Any sports fan is familiar with the term Analytics. There's even a movie about baseball analytics and it nearly won an Oscar for how complicated it was. As science advances, it is to be assumed that many of us are familiar with the basic premise at least.

 

So why does the word Data put us in trouble?

Let's review these terms, the differences between them, and what they mean. After all, getting things right when it comes to Data is absolutely crucial in these times. Big Data is becoming increasingly important in our world and there are thousands of different facets of this concept that are worth exploring.

 

Data Science

Data Science is a broad term in which the scientific method, mathematics, statistics, and many other tools are applied to data sets to extract insights from such data. It essentially makes use of multifaceted tools to deal with Big Data and obtain useful information from it.

 

Data scientists search large data sets where a connection may or may not be easily made, then sharpen this connection to the point where it results in something meaningful from this compilation.

In case you're not excited about the idea of Data Science yet, Harvard Business Review recently named Data Scientists the Sexiest Job of the 21st Century.

 

Data Analytics

Data Analytics or data analysis is similar to Data Science but in a more concentrated way. Think of data analysis at its most basic level as a more focused version of data science where a data set is specifically established to be scanned and analyzed, often with a specific goal in mind.

 

Think back to “Moneyball” the movie we referenced earlier. Those men were data analysts. Why? Because they looked at the aggregate data of all these baseball players that people put aside and found that, through the numbers, these athletes might not be attractive, but the numbers showed that they were effective.

 

Data analysis is the process of defining and searching through those numbers to find out who the 'Moneyball' players were. And it worked. Now teams from all leagues in all sports are somehow applying data analytics to their work.

 

As data science is a relatively new term and there is a lot of discussion about which exactly qualifies as the definitive definition. But what we have here is a beginning. Also, we have to talk about the sexiest job of the century and a movie about baseball, all in a post about Big Data. That is an achievement in itself.

 

Machine learning

Machine Learning is made up of statistical analysis and predictive analysis that is used to detect patterns and capture hidden information based on perceived data.

 

Machine Learning is a type of Artificial Intelligence that is responsible for providing computers with the ability to learn about new data sets without the need to be programmed through an explicit source. It is primarily focused on the development of various computer programs that can predict events when exposed to new data sets.

 

Machine Learning follows the data analysis method that is responsible for automating model building analytically. It uses algorithms that iteratively acquire knowledge of the data and in this process; it allows computers to find seemingly hidden perceptions without the help of an external program.

 

 

Why data science is important

It enables one to understand what it means to apply for that dream job or if you need to make an important hiring. But in addition to that, data science plays a very important role in machine learning and artificial intelligence.

 

Being able to sift through and connect huge amounts of data followed by algorithm and function training that allows virtual entities to learn from that data is highly demanded in today's marketplace.

Machine learning is one of the best developments in the world of technology and it is impressive how it continually innovates.

 

Let's look at the case of IBM Watson and its victory at Jeopardy, or Google's DeepMind, beating the best human players in the world at the board game. Speaking of Google, the company recently bought Kaggle an online community that hosts data science and machine learning skills.

 

The fact is, this technology is the future and Google know it, which is why understanding the differences between these terms is important.

 

At the end of the day, there is nothing to be scared of from each term, they are all essentially data detectives, sorting through large collections of statistics, figures, reports, etc., until they find the necessary information they came for. How they do it and what the end goal is may be different, but they are not that different.

 

Conclusion

Now you are able to navigate between the ambiguity of these data terms and come out the other side in one piece. But this is only the beginning of the learning, there is much more to the data than these three terms. And as we've mentioned, data is very important. They are becoming more and more prominent in our lives as they take care of everything from sports to business appointments to medicine. Data-driven actions are the present and the foreseeable future, so you can never learn much about Big Data and what it will mean for your life.

 

Also Read,